| Title |
Fast and effective protein model refinement using deep graph neural networks
|
|---|---|
| Published in |
Nature Computational Science, July 2021
|
| DOI | 10.1038/s43588-021-00098-9 |
| Pubmed ID | |
| Authors |
Xiaoyang Jing, Jinbo Xu |
X Demographics
The data shown below were collected from the profiles of 45 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 14 | 31% |
| Spain | 3 | 7% |
| Japan | 2 | 4% |
| China | 1 | 2% |
| Brazil | 1 | 2% |
| Italy | 1 | 2% |
| United Kingdom | 1 | 2% |
| Australia | 1 | 2% |
| Tunisia | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 20 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 34 | 76% |
| Scientists | 8 | 18% |
| Practitioners (doctors, other healthcare professionals) | 2 | 4% |
| Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 85 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 18% |
| Researcher | 11 | 13% |
| Student > Master | 7 | 8% |
| Student > Bachelor | 5 | 6% |
| Student > Postgraduate | 4 | 5% |
| Other | 9 | 11% |
| Unknown | 34 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 18 | 21% |
| Computer Science | 11 | 13% |
| Agricultural and Biological Sciences | 4 | 5% |
| Chemistry | 4 | 5% |
| Engineering | 3 | 4% |
| Other | 9 | 11% |
| Unknown | 36 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 December 2021.
All research outputs
#1,679,743
of 25,734,859 outputs
Outputs from Nature Computational Science
#132
of 606 outputs
Outputs of similar age
#40,265
of 448,224 outputs
Outputs of similar age from Nature Computational Science
#7
of 39 outputs
Altmetric has tracked 25,734,859 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 606 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.5. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 448,224 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.